This project develops and applies new causal inference statistical methodology to investigate: (1) the impact of the time of initiation of Highl Active Anti-Retroviral Treatment (HAART) following infection on short- and long-term immune reconstruction in the beginning stages of HIV-1 infection, (2) the impact of treatment regimes that initiate HAART once CD4 count drops below a certain threshold, possibly depending on viral load, and (3) the impact of immune activation and CD4 counts one year since HAART initiation on long-term beneficial clinical outcomes had all patients remained on suppressive HAART. The first two aims will inform the clinical decision process of when to initiate HAART. Delaying HAART initiation has the advantage of postponing the adverse toxicities from HAART and drug resistance, and hence might improve long-term prognoses. However, delaying initiation can lead to irreversible immune system damage. This major issue in public health is especially relevant in the early phases of HIV-infection, for which no firm treatment guidelines exist, and even more so in view of the current CDC efforts to encourage early diagnosis of HIV-infection. The third aim is important because it can help decide whom to target with new therapies besides HAART and inform studies of the effect of such new therapies. For all three aims, randomized clinical trials may be unethical, since they would entail forcing patients to continue being off or on treatment over prolonged periods of time after baseline. Therefore, the field has to base these treatment strategies on analyses of observational data, where the treatment decisions are not randomized, and sicker patients are more likely to initiate treatment earlier and discontinue treatment later. Thus, estimation methods must adequately adjust for the patient characteristics that confound the relationship between times of initiating treatment and the outcome of interest. This proposal outlines new causal inference methods based on a new class of Structural Nested Mean Models and a new type of Inverse Probability of Censoring Weighting to adjust for this confounding by indication, since previous methods are not directly applicable to our settings. These new methods generate a large class of estimators, and naive choices of estimators are inefficient in that they lead to large standard errors. Thus, with those naive choices, models with many parameters encoding the dependence of the treatment effect on current covariates cannot well be estimated in samples of reasonable size. This proposal addresses many unresolved issues in this methodology and its applications. The objectives of this proposal are three-fold. The first objective is to develop accurate and precise estimators, using theory about semiparametric models, to be submitted to methodological journals. The second objective is to apply these methods, the results of which will be submitted to journals about HIV/AIDS. And the third objective is to make the resulting software publicly available for researchers in HIV/AIDS and other fields. These methods may have a great impact on other investigations, since time-dependent confounding by treatment indication is a very common problem that leads to bias in the absence of proper statistical methods like the ones proposed here.